ASE-5036 Optimal Estimation and Prediction Based on Models, 7 cr
Additional information
Suitable for postgraduate studies
Person responsible
Robert Piche
Lessons
Implementation 1: ASE-5036 2015-01
| Study type | P1 | P2 | P3 | P4 | Summer |
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Requirements
Exam, homework and computer exercises, term project.
Completion parts must belong to the same implementation
Learning Outcomes
The student can apply modern algorithms of Bayesian filtering and smoothing. Student is capable of (grade (3/5)) 1. using the basic concepts and formulas of probability and Bayesian statistical inference. 2. presenting a time-series estimation problem in a state-space form and understanding its statistical assumptions and limitations. 3. implementing the Kalman filter and the most common approximations of the nonlinear Bayesian filter and smoother. 4. understanding the approximations and limitations of different non-linear filters. 5. estimating static parameters of the state space model. Grade (1/5): the goal 4 and at least two other goals achieved
Content
| Content | Core content | Complementary knowledge | Specialist knowledge |
| 1. | Multivariate probability basics and the multivariate Gaussian distribution. | Chebyshev inequality | Laws of total expectation and total variance |
| 2. | Kalman filter | Stationary Kalman filter, information filter, missing measurement | discretisation of stochastic differential equation; Joseph formula |
| 3. | EKF, UKF, bootstrap particle filter | EKF2, GHKF, importance sampling, SIR | stratified resampling, RB particle filter |
| 4. | RTS smoother | RTS extensions; particle smoother | fixed-lag smoothing; fixed-point smoothing |
| 5. | State-space mode parameter estimation using MCMC | Parameter estimation using EM |
Study material
| Type | Name | Author | ISBN | URL | Additional information | Examination material |
| Book | Bayesian Filtering and Smoothing | Simo Särkkä | 9781107619289 | Yes |
Prerequisites
| Course | Mandatory/Advisable | Description |
| ASE-2510 Johdatus systeemien analysointiin | Advisable |
Additional information about prerequisites
Knowledge of dynamic system modeling and probability from any suitable course is sufficient.
Correspondence of content
| Course | Corresponds course | Description |
| ASE-5036 Optimal Estimation and Prediction Based on Models, 7 cr | ASE-5030 Optimal Estimation and Prediction Based on Models, 7 cr | |
| ASE-5036 Optimal Estimation and Prediction Based on Models, 7 cr | ACI-42136 Stochastic Estimation and Control, 5 cr | |
|
ACI-21086 Control System Design with Matlab, 5 cr + ACI-42066 Robust Control, 5 cr + ASE-5036 Optimal Estimation and Prediction Based on Models, 7 cr | ACI-42086 Optimal and Robust Control System Design with Matlab, 7 cr | |
| ASE-5036 Optimal Estimation and Prediction Based on Models, 7 cr | ASE-5037 Model-Based Estimation, 5-7 cr | |
| ASE-5036 Optimal Estimation and Prediction Based on Models, 7 cr | ASE-5036 Optimal Estimation and Prediction Based on Models, 7 cr |